Analysis of Web Logs And Web User In Web Mining

Grace, L. K. Joshila; Maheswari, V. Uma; Nagamalai, Dhinaharan

doi:10.5121/ijnsa.2011.3107

Cited by 42 publications

(8 citation statements)

References 3 publications

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“…To use log files in the e-assessment system, we have to implement specific methods to extract information from these files. Log files contain information about Learner Name, IP Address, Time Stamp, Access Request, number of Transferred Bytes, Result Status, and Referred URL [24]. By analyzing these log files, we give a neat idea about the learner, her/him interaction with the learning system.…”

Section: Profile Informationmentioning

confidence: 99%

Learning analytics for the development of adapted e‐assessment workflow system

Hlaoui

Hajjej

Ayed

2016

Comp Applic In Engineering

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Learning analytics (LA) is a significant area of the enhanced learning technology that has been emerged during the last decade. In this paper, we present a Cloud Adapted Workflow e-Assessment System, called Cloud-AWAS. This system makes use of the learning analytics in order to turn learners into more effective and better learners. Hence, Cloud-AWAS could be seamlessly integrated into any learning management system. This system provides a generic e-assessment workflow which is adapted to learner's profiles. We have started by creating a learner profile ontology based on extraction data from e-assessment activities, file log and personal information. Then, we have defined three adaptation actions: Add Activity, Edit Activity, and Delete Activity, applied on the workflow assessment and using information extracted from learner profile ontology instances. Each action is applied according to a number of conditions. Finally, we present some results of the empirical evaluation of Cloud_AWAS. ß 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:951-966, 2016; View this article online at wileyonlinelibrary.com/journal/cae;In addition of these challenges, we have proposed a new challenge which addresses: how to keep the semantic relationships between different data to avoid losing real information. Hence, we propose to use ontologies to describe learner profile in order to preserve different semantic relationships. In addition, ontology helps to analyze the knowledge about learner behavior.Correspondence to F. Hajjej (hajjejfahima@gmail.com).

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Section: Profile Informationmentioning

confidence: 99%

Learning analytics for the development of adapted e‐assessment workflow system

Hlaoui

Hajjej

Ayed

2016

Comp Applic In Engineering

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“…Web scraping is a technology that allows the taking of resources from the web and the results can be utilized again by other systems. The process of retrieving data or information from sites on the internet is called web scraping [3], [4], [5]; web extraction [6], [7], [8]; web mining [9], [10]; and web harvesting [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Web Scraping with HTML DOM Method for Data Collection of Scientific Articles from Google Scholar

Rahmatulloh¹,

Gunawan²

2020

Indonesian J. of Inf. Syst.

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Abstrak. Google Scholar adalah layanan berbasis web untuk mencari literatur akademik. Berbagai jenis referensi yang dapat diakses contohnya adalah: makalah peer-review, tesis, buku, abstrak dan artikel dari penerbit akademik, komunitas profesional, pusat data pra-cetak, universitas dan organisasi akademik lainnya. Google Scholar menyediakan fitur pembuatan profil untuk setiap peneliti, pakar, dan dosen. Jumlah publikasi dari lembaga akademis bersama dengan data yang detail tentang publikasi artikel ilmiah dapat diakses melalui Google Scholar. Rekapitulasi publikasi artikel ilmiah dari masing-masing peneliti di suatu lembaga diperlukan untuk menentukan kinerja penelitian secara kolektif. Namun, hal ini masih menyisakan masalah, yaitu belum tersedianya layanan rekapitulasi publikasi artikel ilmiah untuk setiap peneliti di suatu lembaga. Oleh karena itu, penelitian ini berupaya melakukan rekapitulasi publikasi artikel ilmiah. Pengumpulan data dari Google Scholar dilakukan dengan menerapkan teknologi web scrapping. Eksperimen web scrapping dari Google Scholar dalam penelitian ini telah berhasil mengambil 238 data peneliti dan 2.523 file artikel. Data yang telah diunduh disimpan ke dalam basis data, kemudian digunakan untuk rekapitulasi publikasi artikel ilmiah, yang dapat menampilkan: daftar profil peneliti, daftar afiliasi, daftar kutipan, dan daftar judul artikel yang dapat dicetak dalam bentuk *.pdf atau *.xlsx dan dilengkapi dengan pencarian data dan fitur penyortiran.Abstract. Google Scholar is a web-based service for searching broad academic literature. Various types of references can be accessed such as: peer-reviewed papers, theses, books, abstracts and articles from academic publishers, professional communities, pre-printed data center, universities and other academic organizations. Google Scholar provides the profile creation feature of every researcher, expert, and lecturer. The quantity of publication from an academic institution along with detailed data on the publication of scientific articles can be accessed through Google Scholar. Recapitulation of the publication of scientific articles of each researcher in an institution is needed to determine the research performance collectively. However, it still leaves a problem, that is the unavailability of recapitulation services publication of scientific articles for each researcher in an institution. Therefore, this study attempts to make the recapitulation of scientific article publications. Data collection from Google Scholar was carried out by applying web scraping technology. The scraping experiment from Google Scholar in this study has succeeded in retrieving 238 researchers' data and 2,523 article files. The data that had been downloaded was stored in a database, then used to recapitulate the publication of scientific articles, which can display: a list of researcher profiles, a list of affiliations, a list of

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“…Analyzing these log files using data mining reveal many interesting patterns (Mahoui, Application of data mining on web usage data for security et al, 2001). The purpose of web mining is to identify potential users for the web (Joshila et al, 2011;Thakkar and Rao, 2013). Web usage mining technique is useful in predicting and investigates the user behavioral from web log files (Singh and Jain, 2014).…”

Section: Introductionmentioning

confidence: 99%

Application of Data Mining on Web Usage Data for Security: WebSecuDMiner

Khan¹,

Park²

2019

Preprint

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The purpose of this paper is to develop WebSecuDMiner algorithm to discover unusual web access patterns based on analysing the potential rules hidden in web server log and user navigation history. Design/methodology/approach: WebSecuDMiner uses equivalence class transformation (ECLAT) algorithm to extract user access patterns from the web log data, which will be used to identify the user access behaviours pattern and detect unusual one. Data extracted from the web serve log and user browsing behaviour is exploited to retrieve the web access pattern that is produced by the same user. Findings: WebSecuDMiner is used to detect whether any unauthorized access have been posed and take appropriate decisions regarding the review of the original rights of suspicious user. Research limitations/implications: The present work uses the database which is extracted from web serve log file and user browsing behaviour. Although the page is viewed by the user, the visit is not recorded in the server log file, since it can be access from the browser's cache.

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Analysis of Web Logs And Web User In Web Mining

Abstract: ABSTRACT

Cited by 42 publications

References 3 publications

Learning analytics for the development of adapted e‐assessment workflow system

Learning analytics for the development of adapted e‐assessment workflow system

Web Scraping with HTML DOM Method for Data Collection of Scientific Articles from Google Scholar

Application of Data Mining on Web Usage Data for Security: WebSecuDMiner

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